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Multi-source heterogeneous iris recognition using Locality Preserving Projection

EasyChair Preprint no. 1386

8 pagesDate: August 9, 2019


Multi-source heterogeneous iris recognition(MSH-IR) has be- come one of the most challenging hot issues. Iris recognition is too depen- dent on the acquisition device, causing have large intra-class variations, capture iris duplicate data more and more larger. The paper proposed the application of locality preserving projection (LPP) algorithm based on manifold learning as a framework for MSH-IR. Looking for similar in- ternal structures of iris texture, MSH-IR is performed by measuring sim- ilarity. The new solution innovation aspects that LPP algorithm is used to establish the neighboring structure of the similar feature points of the iris texture, and the similarity between the MSH-IR structures is mea- sured after mapping to the low-dimensional space, and using the SVM algorithm to nd and establish the optimal classication hyperplane in low-dimensional space to implement the classication of multi-source het- erogeneous iris images. The experiment based on the JLU-MultiDev iris database. The experimental results demonstrate the eectiveness of the LPP dimension reduction algorithm for MSH-IR.

Keyphrases: Iris recognition, LPP, manifold learning, multi-source heterogeneous

BibTeX entry
BibTeX does not have the right entry for preprints. This is a hack for producing the correct reference:
  author = {Guang Huo and Qi Zhang and Huan Guo and Wenyu Li and Yangrui Zhang},
  title = {Multi-source heterogeneous iris recognition using Locality Preserving Projection},
  howpublished = {EasyChair Preprint no. 1386},

  year = {EasyChair, 2019}}
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